
'''simple docstring'''





import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
      from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import MobileNetVaImageProcessor






class 							UpperCAmelCase_							(    SCREAMING_SNAKE_CASE_				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.config_class(**self.inputs_dict )
   self.parent.assertTrue(hasattr(snake_case__			,				"tf_padding" ) )
   self.parent.assertTrue(hasattr(snake_case__			,				"depth_multiplier" ) )






class 							UpperCAmelCase_							:

 '''simple docstring'''







 def __init__(					self		:  Union[str, Any]			,				snake_case__		:  Optional[Any]			,				snake_case__		:  int=13			,				snake_case__		:  Optional[int]=3			,				snake_case__		:  str=32			,				snake_case__		:  Union[str, Any]=0.25			,				snake_case__		:  Union[str, Any]=8			,				snake_case__		:  Tuple=8			,				snake_case__		:  int=6			,				snake_case__		:  List[Any]=32			,				snake_case__		:  Optional[Any]=True			,				snake_case__		:  List[Any]=True			,				snake_case__		:  List[str]=True			,				snake_case__		:  Optional[int]="relu6"			,				snake_case__		:  List[str]=12_80			,				snake_case__		:  Any=0.1			,				snake_case__		:  Union[str, Any]=0.02			,				snake_case__		:  List[str]=True			,				snake_case__		:  List[Any]=True			,				snake_case__		:  Any=10			,				snake_case__		:  int=None			,				):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							parent
   UpperCAmelCase__      :					int				       =							batch_size
   UpperCAmelCase__      :					Union[str, Any]				       =							num_channels
   UpperCAmelCase__      :					Optional[Any]				       =							image_size
   UpperCAmelCase__      :					Tuple				       =							depth_multiplier
   UpperCAmelCase__      :					List[Any]				       =							depth_divisible_by
   UpperCAmelCase__      :					Optional[int]				       =							min_depth
   UpperCAmelCase__      :					Any				       =							expand_ratio
   UpperCAmelCase__      :					Tuple				       =							tf_padding
   UpperCAmelCase__      :					str				       =							output_stride
   UpperCAmelCase__      :					int				       =							first_layer_is_expansion
   UpperCAmelCase__      :					Tuple				       =							finegrained_output
   UpperCAmelCase__      :					List[Any]				       =							hidden_act
   UpperCAmelCase__      :					List[Any]				       =							last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
   UpperCAmelCase__      :					int				       =							classifier_dropout_prob
   UpperCAmelCase__      :					List[Any]				       =							use_labels
   UpperCAmelCase__      :					str				       =							is_training
   UpperCAmelCase__      :					Any				       =							num_labels
   UpperCAmelCase__      :					str				       =							initializer_range
   UpperCAmelCase__      :					List[Any]				       =							scope







 def 							UpperCamelCase  (					self		:  int ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

   UpperCAmelCase__      :					Optional[int]				       =							None
   UpperCAmelCase__      :					Tuple				       =							None
   if self.use_labels:
     UpperCAmelCase__      :					Any				       =							ids_tensor([self.batch_size]			,				self.num_labels )
     UpperCAmelCase__      :					Tuple				       =							ids_tensor([self.batch_size, self.image_size, self.image_size]			,				self.num_labels )

   UpperCAmelCase__      :					Union[str, Any]				       =							self.get_config()

   return config, pixel_values, labels, pixel_labels







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   return MobileNetVaConfig(
       num_channels=self.num_channels			,				image_size=self.image_size			,				depth_multiplier=self.depth_multiplier			,				depth_divisible_by=self.depth_divisible_by			,				min_depth=self.min_depth			,				expand_ratio=self.expand_ratio			,				output_stride=self.output_stride			,				first_layer_is_expansion=self.first_layer_is_expansion			,				finegrained_output=self.finegrained_output			,				hidden_act=self.hidden_act			,				tf_padding=self.tf_padding			,				classifier_dropout_prob=self.classifier_dropout_prob			,				initializer_range=self.initializer_range			,				)







 def 							UpperCamelCase  (					self		:  List[Any]			,				snake_case__		:  Union[str, Any]			,				snake_case__		:  str			,				snake_case__		:  str			,				snake_case__		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							MobileNetVaModel(config=snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					Optional[Any]				       =							model(snake_case__ )
   self.parent.assertEqual(
       result.last_hidden_state.shape			,				(
           self.batch_size,
           self.last_hidden_size,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )			,				)
   self.parent.assertEqual(
       result.pooler_output.shape			,				(self.batch_size, self.last_hidden_size)			,				)







 def 							UpperCamelCase  (					self		:  Tuple			,				snake_case__		:  Tuple			,				snake_case__		:  List[Any]			,				snake_case__		:  Any			,				snake_case__		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							self.num_labels
   UpperCAmelCase__      :					str				       =							MobileNetVaForImageClassification(snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					int				       =							model(snake_case__			,				labels=snake_case__ )
   self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels) )







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Dict			,				snake_case__		:  int			,				snake_case__		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							self.num_labels
   UpperCAmelCase__      :					List[str]				       =							MobileNetVaForSemanticSegmentation(snake_case__ )
   model.to(snake_case__ )
   model.eval()
   UpperCAmelCase__      :					int				       =							model(snake_case__ )
   self.parent.assertEqual(
       result.logits.shape			,				(
           self.batch_size,
           self.num_labels,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )			,				)
   UpperCAmelCase__      :					Any				       =							model(snake_case__			,				labels=snake_case__ )
   self.parent.assertEqual(
       result.logits.shape			,				(
           self.batch_size,
           self.num_labels,
           self.image_size // self.output_stride,
           self.image_size // self.output_stride,
       )			,				)







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					Union[str, Any]				       =							self.prepare_config_and_inputs()
   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					List[Any]				       =							config_and_inputs
   UpperCAmelCase__      :					Any				       =							{"pixel_values": pixel_values}
   return config, inputs_dict






@require_torch
class 							UpperCAmelCase_							(    SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    str				=							(
     (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 lowercase_					:    List[Any]				=							(
     {
         "feature-extraction": MobileNetVaModel,
         "image-classification": MobileNetVaForImageClassification,
         "image-segmentation": MobileNetVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase_					:    int				=							False
 lowercase_					:    Tuple				=							False
 lowercase_					:    List[Any]				=							False
 lowercase_					:    Any				=							False







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							MobileNetVaModelTester(self )
   UpperCAmelCase__      :					int				       =							MobileNetVaConfigTester(self			,				config_class=snake_case__			,				has_text_modality=snake_case__ )







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   self.config_tester.run_common_tests()







 @unittest.skip(reason="MobileNetV2 does not use inputs_embeds" )
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   pass







 @unittest.skip(reason="MobileNetV2 does not support input and output embeddings" )
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   pass







 @unittest.skip(reason="MobileNetV2 does not output attentions" )
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   pass







 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					List[Any]				       =							model_class(snake_case__ )
     UpperCAmelCase__      :					int				       =							inspect.signature(model.forward )
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     UpperCAmelCase__      :					Union[str, Any]				       =							[*signature.parameters.keys()]

     UpperCAmelCase__      :					List[str]				       =							["pixel_values"]
     self.assertListEqual(arg_names[:1]			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case__ )







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   def check_hidden_states_output(snake_case__		:  int			,				snake_case__		:  Tuple			,				snake_case__		:  Tuple ):
     UpperCAmelCase__      :					Optional[Any]				       =							model_class(snake_case__ )
     model.to(snake_case__ )
     model.eval()

     with torch.no_grad():
       UpperCAmelCase__      :					int				       =							model(**self._prepare_for_class(snake_case__			,				snake_case__ ) )

     UpperCAmelCase__      :					Any				       =							outputs.hidden_states

     UpperCAmelCase__      :					List[str]				       =							16
     self.assertEqual(len(snake_case__ )			,				snake_case__ )

   UpperCAmelCase__       ,			UpperCAmelCase__      :					Dict				       =							self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     UpperCAmelCase__      :					int				       =							True
     check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__ )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     UpperCAmelCase__      :					Union[str, Any]				       =							True

     check_hidden_states_output(snake_case__			,				snake_case__			,				snake_case__ )







 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*snake_case__ )







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__ )







 @slow
 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     UpperCAmelCase__      :					Dict				       =							MobileNetVaModel.from_pretrained(snake_case__ )
     self.assertIsNotNone(snake_case__ )







def snake_case_     (					):
  UpperCAmelCase__      :					str				       =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






@require_torch
@require_vision
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 @cached_property
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   return (
       MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224" ) if is_vision_available() else None
   )







 @slow
 def 							UpperCamelCase  (					self		:  Optional[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Tuple				       =							MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224" ).to(snake_case__ )

   UpperCAmelCase__      :					str				       =							self.default_image_processor
   UpperCAmelCase__      :					List[str]				       =							prepare_img()
   UpperCAmelCase__      :					Dict				       =							image_processor(images=snake_case__			,				return_tensors="pt" ).to(snake_case__ )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase__      :					List[Any]				       =							model(**snake_case__ )

   # verify the logits
   UpperCAmelCase__      :					Optional[Any]				       =							torch.Size((1, 10_01) )
   self.assertEqual(outputs.logits.shape			,				snake_case__ )

   UpperCAmelCase__      :					str				       =							torch.tensor([0.2445, -1.1993, 0.1905] ).to(snake_case__ )

   self.assertTrue(torch.allclose(outputs.logits[0, :3]			,				snake_case__			,				atol=1e-4 ) )







 @slow
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )
   UpperCAmelCase__      :					Any				       =							model.to(snake_case__ )

   UpperCAmelCase__      :					List[Any]				       =							MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513" )

   UpperCAmelCase__      :					List[str]				       =							prepare_img()
   UpperCAmelCase__      :					List[str]				       =							image_processor(images=snake_case__			,				return_tensors="pt" ).to(snake_case__ )

   # forward pass
   with torch.no_grad():
     UpperCAmelCase__      :					Tuple				       =							model(**snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							outputs.logits

   # verify the logits
   UpperCAmelCase__      :					Optional[int]				       =							torch.Size((1, 21, 65, 65) )
   self.assertEqual(logits.shape			,				snake_case__ )

   UpperCAmelCase__      :					Any				       =							torch.tensor(
       [
           [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
           [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
           [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
       ]			,				device=snake_case__			,				)

   self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,				snake_case__			,				atol=1e-4 ) )




import functools
from typing import Any


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				list[str]							)   ->    bool:

		# Validation
		if not isinstance(UpperCamelCase			,       UpperCamelCase							) or len(UpperCamelCase							) == 0:
				raise ValueError('the string should be not empty string'							)

		if not isinstance(UpperCamelCase			,       UpperCamelCase							) or not all(
		    isinstance(UpperCamelCase			,       UpperCamelCase							) and len(UpperCamelCase							) > 0 for item in words							):
				raise ValueError('the words should be a list of non-empty strings'							)

		# Build trie
		_lowerCamelCase				   =  {}
		_lowerCamelCase				   =  'WORD_KEEPER'

		for word in words:
				_lowerCamelCase				   =  trie
				for c in word:
						if c not in trie_node:
								_lowerCamelCase				   =  {}

						_lowerCamelCase				   =  trie_node[c]

				_lowerCamelCase				   =  True

		_lowerCamelCase				   =  len(UpperCamelCase							)

		# Dynamic programming method
		@functools.cache
		def is_breakable(UpperCamelCase     :				int							) -> bool:
				if index == len_string:
						return True

				_lowerCamelCase				   =  trie
				for i in range(UpperCamelCase			,       UpperCamelCase							):
						_lowerCamelCase				   =  trie_node.get(string[i]			,       UpperCamelCase							)

						if trie_node is None:
								return False

						if trie_node.get(UpperCamelCase			,       UpperCamelCase							) and is_breakable(i + 1							):
								return True

				return False

		return is_breakable(0							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()